Vision • To establish a dialogue between statisticians, clinicians, regulators and other lines within the Pharmaceutical Industry, Health Authorities and Academia, • with a goal to contribute to developing a consensusposition on when and how to consider the use of adaptive designs in clinical drug development.

Mission • To facilitate the implementation adaptive designs, but only where appropriate • To contribute to standardizing the terminology and classification in the rapidly evolving field of adaptive designs • To contribute to educational and information sharing efforts on adaptive designs • To interact with experts within Health Authorities (FDA, EMEA, and others) and Academia to sharpen our thinking on defining the scope of adaptive designs • To support our colleagues in health authorities in their work towards the formulation of regulatory draft guidance documents on the topic of adaptive designs.

Outline • Definition and general structure of adaptive designs • Classification of adaptive designs in drug development • Achieving the goals

What are Adaptive Designs? Flexible Multi-stage Response-driven Dynamic Self-designing Sequential Novel ADAPTIVE • An adaptive design should be adaptive by "design" not an adhoc change of the trial conduct and analysis • Adaptation is a prospective design feature, not a remedy for poor planning

Adaptive Design uses accumulating data to decide on how to modify aspects of the study without undermining the validity and integrity of the trial Validitymeans providing correct statistical inference (such as adjusted p-values, unbiased estimates and adjusted confidence intervals, etc) assuring consistency between different stages of the study minimizing operational bias Definition Integritymeans • providing convincing results to a broader scientific community • preplanning, as much as possible, based on intended adaptations • maintaining confidentiality of data

General Structure • An adaptive design requires the trial to be conducted in several stages with access to the accumulated data • An adaptive design may have one or more rules: • Allocation Rule: how subjects will be allocated to available arms • Sampling Rule: how many subjects will be sampled at next stage • Stopping Rule: when to stop the trial (for efficacy, harm, futility) • Decision Rule: the terminal decision rule and interim decisions pertaining to design change not covered by the previous three rules • At any stage, the data may be analyzed and next stages redesigned taking into account all available data

Decision Rules • Changing the test statistics • Adaptive scores in trend test or under non proportional hazards • Adaptive weight in location-scale test • Including a covariate that shows variance reduction • Redesigning multiple endpoints • Changing their pre-assigned hierarchical order in multiple testing • Updating their correlation in reverse multiplicity situation • Switching from superiority to non-inferiority • Changing the hierarchical order of hypotheses • Changing the patient population • going forward either with the full population or with a pre-specified subpopulation

Bayesian Designs • Objective: to use the posterior probabilities of hypotheses of interest as a basis for interim decisions (Proper Bayesian)or to explicitly assess the losses associated with consequences of stopping or continuing the study (Decision-theoretic Bayesian) • AR: equal randomization or play-the-winner (next patient is allocated to the currently superior treatment) or bandit designs (minimizing the number of patients allocated to the inferior treatment) • SaR: not specified • StR: not formally pre-specified stopping criterion, or using a skeptical prior for stopping for efficacy and an enthusiastic prior for stopping for futility, or using backwards induction • DR: update the posterior distribution; formal incorporation of external evidence; inference not affected by the number and timing of IAs • References: Berry (2001, 2004); Berry et al. (2001); Spiegelhalter et al. (2004).

Pairwise comparisons with GSD • Objective: compare multiple treatments with a control; focus on type I error rate rather than power • A simple Bonferroni approximation is only slightly conservative • Treatments may be dropped in the course of the trial if they are significantly inferior to others • “Step-down” procedures allow critical values for remaining comparisons to be reduced after some treatments have been discarded • References: Follmann et al (1994)

p-value combination tests • Objective: compare multiple treatments with a control in a two-stage design allowing integration of data from both stages in a confirmatory trial • Focus: control of multiple (familywise) Type I error level • Great flexibility: • General distributional assumptions for the endpoints • General stopping rules and selection criteria • Early termination of the trial • Early elimination of treatments due to lack of efficacy or to safety issues or for ethical/economic reasons • References: Bauer&Kieser (1994); Liu&Pledger (2005)

Seamless Designs • Two-stage adaptive designs • 1st Stage: treatment (dose) selection – “learning” • 2nd Stage: comparison with control – “confirming” • Treatment selection may be based on a short-term endpoint (surrogate), while confirmation stage uses a long-term (clinical) endpoint • 2nd Stage data and the relevant groups from 1st Stage data are combined in a way that • Guarantees the Type I error rate for the comparison with control • Produces efficient unbiased estimates and confidence intervals with correct coverage probability

Selection and testing • Objective: to select the “best” treatment in the 1st stage and proceed to the 2nd stage to compare with control • Focus: • overall type I error rate is maintained (TSE) • trial power is also achieved (ST) • selection is based on surrogate (or short-term) endpoint (TS) • Method includes: • early termination of the whole trial • early elimination of inferior treatments • References: Thall,Simon&Ellenberg; Stallard&Todd; Todd&Stallard

Achieving the goals • The objective of a clinical trial may be either • to target the MTD or MED or to find the therapeutic range • or to determine the OSD (Optimal Safe Dose) to be recommended for confirmation • or to confirm efficacy over control in Phase III clinical trial • This clinical goal is usually determined by • the clinicians from the pharmaceutical industry • practicing physicians • key opinion leaders in the field, and • the regulatory agency

Achieving the goals • Once agreement has been reached on the objective, it is the statistician's responsibility to provide the appropriate design and statistical inferential structure required to achieve that goal

Achieving the goals • There are plenty of available designs on statistician’s shelf • The greatest challenge is their implementation • Adaptive designs have much more to offer than the rigid conventional parallel group designs in clinical trials